Communication systems and methods for cognitive interference mitigation

ABSTRACT

Systems and methods for operating a receiver. The methods comprise: receiving, at the receiver, a combined waveform comprising a combination of a desired signal and an interference signal; and performing, by the receiver, demodulation operations to extract the desired signal from the combined waveform. The demodulation operations comprise: obtaining, by the receiver, machine learned models for recovering the desired signal from combined waveforms; comparing, by the receiver, at least one characteristic of the received combined waveform to at least one of the machine learned models; and determining a value for at least one symbol of the desired signal based on results of the comparing.

BACKGROUND Statement of the Technical Field

The present disclosure relates generally to communication systems. Moreparticularly, the present disclosure relates to communication systemsand methods for cognitive interference mitigation.

Description of the Related Art

Wireless communication systems exist today and are used in variousapplications. In the wireless communication systems, desired signals aresubject to co-channel communication interference at receivers. Theinterference may be unintentional (e.g., a Long Term Evolution (LTE)signal interfering with satellite ground-station communications) orintentional (e.g., an adversary jamming/spoofing militarycommunications).

Some of these systems employ interference cancellation to improvewireless communications. The interference cancellation solution mayrequire use of multiple receive antennas. During operations, each of thereceive antennas receives a waveform including a combination of thedesired signal and an interference signal. The received waveforms arethen analyzed to determine how to form a beam and/or steer a beamtowards the transmitter of the desired signal. The beam forming andsteering allow for cancelation or removal of the interference signalfrom the received waveforms.

Since receive antennas are expensive, other interference cancellationtechniques have been developed which require only a single receiveantenna. In the signal receive antenna scenarios, the system acquiresand estimates all parameters involved in the production of theinterference signal. This is quite difficult to achieve since some ofthese parameters may be subject to noise in the receiver and the desiredsignal interferes with the interference signal. Once estimates areobtained for all of the parameters, the interference signal is thenreconstructed and subtracted out of the received signal to eliminate orminimize any interference with the desired signal. Residual noise fromimperfect parameter estimation decreases the Signal to Noise Ratio (SNR)on the desired signal. Additionally, the single receiver-based solutionis resource and computationally intensive.

SUMMARY

The present disclosure concerns implementing systems and methods foroperating a receiver. The methods comprise: receiving, at the receiver,a combined waveform comprising a combination of a desired signal and aninterference signal; and performing, by the receiver, demodulationoperations to extract the desired signal from the combined waveform. Thedemodulation operations comprise: obtaining, by the receiver, machinelearned models for recovering the desired signal from combinedwaveforms; comparing, by the receiver, at least one characteristic ofthe received combined waveform to at least one of the machine learnedmodels; and determining a value for at least one symbol of the desiredsignal based on results of the comparing and/or a previous symboldecision.

The machine learned models may be generated using the same machinelearning algorithm or respectively using different machine learningalgorithms. A machine learned model may be selected from the machinelearned models for use in the comparing based on results of a gametheory analysis of the machine learned models. The characteristic of thereceived combined waveform may include, but is not limited to, a phasecharacteristic, an amplitude characteristic, a frequency characteristic,or a waveform shape.

The results of the comparing may comprise an identification of a machinelearned model that matches the at least one characteristic of thereceived combined waveform by a given amount. The value for the at leastone symbol of the desired signal may be set equal to a symbol value thatis associated with the identified machine learned model.

In some scenarios, the demodulation operations comprise comparingcharacteristic(s) of each segment of a plurality of segments of thecombined waveform to the machine learned models to identify a machinelearned model that matches the characteristic(s) of the segment by agiven amount. At least two segments may both comprise information for atleast one given symbol of the desired signal. Each segment may extendover multiple symbol time periods. A value for a symbol for the desiredsignal contained in the segment of the combined waveform may be equal toa symbol value that is associated with the machine learned model thatmatches the characteristic(s) of the segment by the given amount.

The implementing systems comprise a processor, and a non-transitorycomputer-readable storage medium comprising programming instructionsthat are configured to cause the processor to implement a method forperforming demodulation operations to extract the desired signal fromthe combined waveform.

BRIEF DESCRIPTION OF THE DRAWINGS

The present solution will be described with reference to the followingdrawing figures, in which like numerals represent like items throughoutthe figures.

FIG. 1 is an illustration of an illustrative system.

FIG. 2 is a block diagram of an illustrative receiver.

FIG. 3 is a block diagram of an illustrative symboldemodulator/interference canceller.

FIGS. 4-7 provide graphs that are useful for understanding digitalcommunications.

FIGS. 8-10 provide graphs that are useful for understanding symboldemodulation and interference cancellation operations performed by areceiver.

FIGS. 11-15 provide illustrations that are useful for understandingimplementations of machine learning algorithms in receivers forfacilitating symbol demodulation and interference cancellation.

FIG. 16 provides a graph that is useful for understanding game theory.

FIG. 17 provides a flow diagram for operating a receiver.

DETAILED DESCRIPTION

It will be readily understood that the components of the embodiments asgenerally described herein and illustrated in the appended figures couldbe arranged and designed in a wide variety of different configurations.Thus, the following more detailed description of various embodiments, asrepresented in the figures, is not intended to limit the scope of thepresent disclosure, but is merely representative of various embodiments.While the various aspects of the embodiments are presented in drawings,the drawings are not necessarily drawn to scale unless specificallyindicated.

The present solution may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the present solution is, therefore,indicated by the appended claims rather than by this detaileddescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

Reference throughout this specification to features, advantages, orsimilar language does not imply that all of the features and advantagesthat may be realized with the present solution should be or are in anysingle embodiment of the present solution. Rather, language referring tothe features and advantages is understood to mean that a specificfeature, advantage, or characteristic described in connection with anembodiment is included in at least one embodiment of the presentsolution. Thus, discussions of the features and advantages, and similarlanguage, throughout the specification may, but do not necessarily,refer to the same embodiment.

Furthermore, the described features, advantages and characteristics ofthe present solution may be combined in any suitable manner in one ormore embodiments. One skilled in the relevant art will recognize, inlight of the description herein, that the present solution can bepracticed without one or more of the specific features or advantages ofa particular embodiment. In other instances, additional features andadvantages may be recognized in certain embodiments that may not bepresent in all embodiments of the present solution.

Reference throughout this specification to “one embodiment”, “anembodiment”, or similar language means that a particular feature,structure, or characteristic described in connection with the indicatedembodiment is included in at least one embodiment of the presentsolution. Thus, the phrases “in one embodiment”, “in an embodiment”, andsimilar language throughout this specification may, but do notnecessarily, all refer to the same embodiment.

As used in this document, the singular form “a”, “an”, and “the” includeplural references unless the context clearly dictates otherwise. Unlessdefined otherwise, all technical and scientific terms used herein havethe same meanings as commonly understood by one of ordinary skill in theart. As used in this document, the term “comprising” means “including,but not limited to”.

As noted above, desired communication signals received over the air maybe susceptible to in-band interference. The in-band interference can becaused by intentional jamming (e.g., adversarial jamming/spoofing ofmilitary communications) or unintentional interference from in-bandsources (e.g., in-band/co-channel terrestrial communications signalsinterfering with space-communication ground stations). Conventionalsolutions to the in-band interference issue utilize interferencecancellation. Interference cancellation generally involves: estimatingparameters of an interference signal; reconstructing the interferencesignal using the estimated parameters; and subtracting the reconstructedinterference signal from a received composite signal to obtain thedesired signal. Once all interference signals have been removed from thereceived composite signal, the original communications data isdemodulated. The interference cancellation solution is relativelyresource intensive.

The present solution provides a novel way to overcome this drawback ofconventional communication systems by employing a machine learningapproach to quickly recognize and classify digitally-modulated symbolsfrom noise and interference. The machine learning approach directlydemodulates the digital communications data from the received compositesignal which includes interference signal(s) and/or noise. The machinelearning approach can include, but is not limited to, a deep learningbased approach to directly recognize desired data symbols/bits. Themachine learning approach is implemented by a neural network that istrained to make decisions on subsets of an overall window of data. Thewindow extends over multiple data symbol periods and provides context toassist the neural network in deciding how to properly demodulate thedesired signal. The trained neural network is able to recognize a-prioriexpected features of a desired signal and reject everything else.

The present solution can be implemented by methods for operating areceiver. The methods comprise: receiving a combined waveform comprisinga combination of a desired signal and an interference signal; andperforming demodulation operations to extract the desired signal fromthe combined waveform. The demodulation operations comprise: obtainingmachine learned models for recovering the desired signal from combinedwaveform; optionally selecting one or more machine learned models basedon a given criteria (e.g., based on results of a game theory analysis ofthe machine learned models); comparing characteristic(s) of the receivedcombined waveform to the machine learned model(s); and determining avalue for at least one symbol of the desired signal based on results ofthe comparing and/or a previous symbol decision. The machine learnedmodels may be generated using the same machine learning algorithm orrespectively using different machine learning algorithms. Thecharacteristic(s) of the received combined waveform may include, butis(are) not limited to, a phase characteristic (e.g., a phase changeover time), an amplitude characteristic (e.g., an average amplitude overtime), a frequency characteristic (e.g., a change in frequency overtime), or a waveform shape.

The results of the comparing may comprise an identification of a machinelearned model that matches the characteristic(s) of the receivedcombined waveform by a given amount (e.g., >50%). The value for thesymbol(s) of the desired signal may be set equal to symbol value(s) thatis(are) associated with the identified machine learned model.

In some scenarios, the methods comprise comparing characteristic(s) ofeach segment of a plurality of segments of the combined waveform to themachine learned models to identify a machine learned model that matchesthe characteristic(s) of the segment by a given amount. At least twosegments may both comprise information for at least one given symbol ofthe desired signal. Each segment may extend over multiple symbol timeperiods. A value for a symbol for the desired signal contained in thesegment of the combined waveform may be set equal to a symbol value thatis associated with the machine learned model that matches thecharacteristic(s) of the segment by the given amount.

The implementing systems of the present solution may comprise aprocessor, and a non-transitory computer-readable storage mediumcomprising programming instructions that are configured to cause theprocessor to implement above described method.

Referring now to FIG. 1, there is provided an illustration of anillustrative communications system 100. Communications system 100 isgenerally configured to allow communications amongst communicationdevices 100, 102 with improved signal demodulation and interferencecancellation. In this regard, the communications system 100 comprises atransmitter 102 and a receiver 104. Transmitter 102 is configured togenerate an analog signal and communicate the same to a receiver 104over a communications link 106. At the receiver 104, machine learningoperations are performed to quickly extract a desired signal from areceived signal comprising noise and/or in-band, interference signal(s)communicated from at least one other transmitter 108 via communicationslink(s) 110. Transmitters are well known in the art.

A more detailed diagram of the receiver 200 is provided in FIG. 2.Receiver 104 of FIG. 1 can be the same as or substantially similar toreceiver 200. As such, the discussion of receiver 200 is sufficient forunderstanding receiver 104 of FIG. 1.

As shown in FIG. 2, receiver 200 is comprised of an antenna 202, anRF-to-IF converter 204, an Analog-to-Digital Converter (ADC) 206, and asymbol demodulator/interference canceller 214. Antenna 202 is configuredto receive signals transmitted from transmitters (e.g., transmitter 102and/or 108 of FIG. 1). Antenna 202 is also configured to communicatereceived signals to RF-to-IF converter 204. RF-to-IF converter 204 isconfigured to translate in frequency a relatively high-frequency RFsignal to a different frequency IF signal. Apparatus and methods forperforming RF-to-IF conversions are well known in the art. Any known orto be known apparatus or method for performing RF-to-IF conversions canbe used herein. The output of the RF-to-IF converter 204 is passed tothe input of the Analog-to-Digital Converter (ADC) 206. ADC 206 isconfigured to convert analog voltage values to digital values, andcommunicate the digital values to the subsequence device 214. Thesubsequent device includes a symbol demodulator/interference canceller214.

A more detailed diagram of the symbol demodulator/interference canceller214 is provided in FIG. 3. Symbol demodulator/interference canceller 214implements a machine learning algorithm to perform interferencemitigation for accurately determining symbols of desired signals whichis embedded in interference and/or noise. In this regard, the symboldemodulator/interference canceller 214 includes a plurality ofcomponents shown in FIG. 3. The symbol demodulator/interferencecanceller 214 may include more or fewer components than those shown inFIG. 3. However, the components shown are sufficient to disclose anillustrative solution implementing the present solution. The hardwarearchitecture of FIG. 3 represents one implementation of a representativesymbol demodulator/interference canceller configured to enableextraction of a desired signal from a received combined waveformcomprising noise and/or an interference signal as described herein. Assuch, the symbol demodulator/interference canceller 214 of FIG. 3implements at least a portion of the method(s) described herein.

The symbol demodulator/interference canceller 214 can be implemented ashardware, software and/or a combination of hardware and software. Thehardware includes, but is not limited to, one or more electroniccircuits. The electronic circuits can include, but are not limited to,passive components (e.g., resistors and capacitors) and/or activecomponents (e.g., amplifiers and/or microprocessors). The passive and/oractive components can be adapted to, arranged to and/or programmed toperform one or more of the methodologies, procedures, or functionsdescribed herein.

As shown in FIG. 3, the symbol demodulator/interference canceller 214comprises a processor 304, an interface 306, a system bus 308, a memory310 connected to and accessible by other portions of symboldemodulator/interference canceller 214 through system bus 308, andhardware entities 312 connected to system bus 308. The interface 306provides a means for electrically connecting the symboldemodulator/interference canceller 214 to other circuits of the receiver(e.g., ADC 206, RF-to-IF converter 204, and/or antenna 202 of FIG. 2).

At least some of the hardware entities 312 perform actions involvingaccess to and use of memory 310, which can be a Random Access Memory(RAM), and/or a disk driver. Machine learned models are stored in memory310 for use in demodulation operations as described herein. Hardwareentities 312 can include a disk drive unit 314 comprising acomputer-readable storage medium 316 on which is stored one or more setsof instructions 320 (e.g., software code) configured to implement one ormore of the methodologies, procedures, or functions described herein.The instructions 320 can also reside, completely or at least partially,within the memory 310 and/or within the processor 304 during executionthereof by the symbol demodulator/interference canceller 214. The memory310 and the processor 304 also can constitute machine-readable media.The term “machine-readable media”, as used here, refers to a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions 320. The term “machine-readable media”, as used here, alsorefers to any medium that is capable of storing, encoding or carrying aset of instructions 320 for execution by the processor 304 and thatcause the processor 304 to perform any one or more of the methodologiesof the present disclosure.

The machine learning algorithm(s) of the symbol demodulator/interferencecanceller 214 is(are) trained by generating signals (eithersynthetically or though capturing of real signals) that are expectedfrom transmitter(s) (e.g., transmitter 102 of FIG. 1) of desired signalsand transmitter(s) (e.g., transmitter 108 of FIG. 1) of interferencesignals during operations of a communication system (e.g., communicationsystem 100 of FIG. 1). The generated signals are then analyzed to detectpatterns therein and produce model(s) of what is expected in a givenenvironment. Each machine learned model specifies a pattern of one ormore characteristics of a given combined waveform including a desiredsignal, noise, and/or an interference signal. One or more symbols of thedesired signal is/are associated with the machine learned model(s). Forexample, the binary symbols “01” are associated with a first machinelearned model, while the binary symbols “10” are associated with asecond machine learned model. The present solution is not limited to theparticulars of this example. The machine learned models can be generatedusing the same or different machine learning algorithms.

During operations of the receiver, the machine learned models are usedby the symbol demodulator/interference canceller 214 to (i) recognize adesired signal embedded in interference and/or noise, (ii) directlymitigate the interference and noise, and (iii) determine the symbolsincluded in the original desired signal. This machine learning basedapproach for interference mitigation does not involve estimation oracquisition of interference signals by the receiver. Rather, the machinelearning based approach simply involves comparing the characteristic(s)of a received combined signal to machine learned models to identifywhich machine learned model(s) match(es) the characteristic(s) of thereceived combined signal by a given amount (e.g., >70%), and outputvalues for symbol(s)/bit(s) that are associated with the matchingmachine learned model(s). The output symbol(s)/bit(s) represent(s) thesymbol(s)/bit(s) which should have been received by the receiver from atransmitter of a desired signal. The machine learning based approach isrelatively inexpensive to implement as compared to conventionalinterference mitigation approaches, at least partially because themachine learning exploits the internal or inherent structures ofdigitally modulated communication signals (desired and interferencesignals) in both time and frequency. The inherent structures ofdigitally modulated communication signals provides features that arerecognizable by a machine learning algorithm.

In digital communications, it is assumed that there is a data stream ina received combined signal. For example, as shown in FIGS. 4-7, a datastream 400 is transmitted from a transmitter (e.g., transmitter 102 ofFIG. 1). The data stream includes a sequence of symbols or bits 1101.Each symbol/bit of the data stream 400 is transmitted as a pulsewaveform 402, 502, 602, 702 from the transmitter. Each pulse waveformextends over a finite period of time which is wider than a time index404 representing a symbol/bit. As shown in FIG. 4, the pulse waveform402 of the first symbol/bit overlaps with subsequent symbols/bits. Whenthe pulse waveform 502 of the second symbol/bit is transmitted, thetransmitted waveform represents the sum of the pulse waveforms 402, 502because the first and second symbols/bits are adding together. Thetransmitted waveform for the second symbol/bit is shown in FIG. 5 as acomposite waveform 504 which has an increased amplitude as compared topulse waveform 502. The third bit is a zero which is represented by anegative pulse waveform 602. The negative pulse waveform 602 extends tothe previous symbols/bits facilitating the increased amplitude of thecomposite waveform 504. The transmitted waveform for the thirdsymbol/bit is shown in FIG. 6 as a composite waveform 604 since thefirst, second and third symbols/bits are being added together. Thefourth symbol/bit is a one which is represented by a positive pulsewaveform 702. The positive pulse waveform 702 extends to the previoussymbols/bits, and therefore contributes to the amplitudes of compositewaveforms 504, 604. The transmitted waveform for the fourth symbol/bitis shown in FIG. 7 as a composite waveform 704 since the first, second,third and fourth symbols/bits are being adding together.

As evident from FIGS. 4-7, each digital symbol/bit effects thetransmitted waveform of neighbor symbols/bits. Thus, every time a pulsewaveform is received at the receiver 200, the receiver is receivinginformation about a plurality of symbols/bits rather than just onesymbol/bit. In general, the machine learning structure of the receiver200 is trying to recognize symbols in accordance with the digitalcommunications scheme of FIGS. 4-7 and in the presence ofnoise/interference.

With reference to FIG. 8, a desired signal including a sequence ofsymbols/bits 1111100001 1110100101 1010111111 10 is transmitted from atransmitter (e.g., transmitter 102 of FIG. 1). The transmitted waveformfor this sequence of symbols/bits is shown as waveform 802. Aninterference signal transmitted from another transmitter (e.g.,transmitter 108 of FIG. 1) is shown as waveform 804. The interferencewaveform 804 has a higher power than the desired waveform 802, and therate at which symbols/bits are transmitted is different than the rate atwhich the symbols/bits of the desired signal are transmitted. Thereceiver 200 receives a waveform 806 which is a combination of thedesired waveform 802 and the interference waveform 804.

When performing symbol demodulation, the receiver 200 analyzes thereceived combined waveform 806 over multiple symbol/bit time periods torecover the symbols/bits. More particularly, the symboldemodulator/interference canceller 214 analyzes a portion/segment of areceived signal present within in a given context window 900 shown inFIG. 9 to recover value(s) for the middle symbol(s)/bit(s) (e.g.,symbols/bits 1000 and 1002 of FIG. 10) thereof. The context window isthen slid in time so that another portion/segment of the received signalis analyzed by the symbol demodulator/interference canceller 214 todemodulate next symbol(s)/bit(s). This process is iteratively repeateduntil all of the symbol(s)/bit(s) in the desired signal are demodulated.The context windows can be overlapping or non-overlapping. In thisregard, two portions/segments of the received signal can includeinformation for one or more of the same symbols/bits, or information fornone of the same symbol(s)/bit(s).

Notably, the context window 900 provides context to the symboldemodulator/interference canceller 214 so that the symboldemodulator/interference canceller 214 can observe how an interferencesignal has been impacting the desired signal over a given period oftime. Accordingly, the context window 900 includes information forsymbols/bits that reside prior to and subsequent to the symbols/bits1000, 1002 to be demodulated. In effect, the symboldemodulator/interference canceller 214 implements the machine learningalgorithm that considers the context of signal structure in aneighborhood of the symbol(s)/bit(s) to facilitate recovery of desiredsymbol(s)/bit(s). The machine learning algorithm is trained to identifypatterns of combined waveforms within received signals on windows oftime over multiple symbol/data periods, and to determine values forsymbol(s)/bit(s) of desired waveforms given the identified patterns ofthe combined waveforms present within the received signals.

The machine learning algorithm can employ supervised machine learning,semi-supervised machine learning, unsupervised machine learning, and/orreinforcement machine learning. Each of these listed types of machinelearning algorithms is well known in the art. In some scenarios, themachine learning algorithm includes, but is not limited to, a deeplearning algorithm (e.g., a Residual Neural Network (ResNet)), aRecurrent Neural Network (RNN) (e.g., a Long Short-Term Memory (LSTM)neural network), a decision tree learning algorithm, an association rulelearning algorithm, an artificial neural network learning algorithm, aninductive logic programming based algorithm, a support vector machinebased algorithm, a Bayesian network based algorithm, a representationlearning algorithm, a similarity and metric learning algorithm, a sparsedictionary learning algorithm, a genetic algorithm, a rule-based machinelearning algorithm, and/or a learning classifier system based algorithm.The machine learning process implemented by the present solution can bebuilt using Commercial-Off-The-Shelf (COTS) tools (e.g., SAS availablefrom SAS Institute Inc. of Cary, North Carolina).

An illustrative architecture for a neural network implementing thepresent solution is provided in FIG. 11. As shown in FIG. 11, the neuralnetwork 1100 comprises a module 1202 that sequentially performsiterations or cycles of the machine learning algorithm. During eachiteration or cycle, the module 1102 receives an input x_(i) andgenerates an output h_(i). The input x_(i) is provided in a time domain(e.g., defines a waveform shape), and comprises a portion/segment of thereceived signal within a context window (e.g., context window 900 ofFIG. 9). The output h_(i) comprises binary value(s) forsymbol(s)/bit(s).

The present solution is not limited to the neural network architectureshown in FIG. 11. For example, the neural network can include aplurality of modules that perform the machine learning algorithm (usingrespective inputs) in a parallel manner.

Another illustrative architecture for a neural network implementing thepresent solution is provided in FIG. 12. As shown in FIG. 12, the neuralnetwork 1200 comprises a module 1202 that sequentially performsiterations or cycles of the machine learning algorithm. During eachiteration or cycle, the module 1202 receives a plurality of inputsx_(i-d1), x_(i-d2), . . . , x_(i-dV) and generates an output h_(i). Theinputs are provided in different domains. For example, input x_(i-d1) isprovided in a time domain, and defines a waveform shape of aportion/segment of the received signal within a context window (e.g.,context window 900 of FIG. 9). Input x_(i-d2) is provided in a phasedomain (e.g., defining a change in phase over time), while inputx_(i-dV) is provided in an amplitude domain (e.g., defining an averageamplitude over time), a frequency domain (e.g., defining a change infrequency over time) or other domain. The inputs x_(i-d2), . . . ,x_(i-dV) can be derived using various algorithms that include, but arenot limited to, a Fourier transform algorithm, a power spectral densityalgorithm, a wavelet transform algorithm, and/or a spectrogramalgorithm. The module 1202 compares a combination of the inputs tomachine learned models to determine the binary values for the desiredsignal's symbol(s)/bit(s). The inputs x_(i-d1), x_(i-d2), . . . ,x_(i-dV) may be weighted differently by the machine learning algorithmemployed by module 1202. The weights can be pre-defined, or dynamicallydetermined based on characteristic(s) of the received combined waveform.

The present solution is not limited to the neural network architectureshown in FIG. 12. For example, the neural network can include aplurality of modules that perform the machine learning algorithm (usingrespective inputs) in a parallel manner.

In recurrent neural network scenarios, the receiver uses reasoning aboutprevious symbol/bit decisions to inform later symbol/bit decisions. Anillustrative architecture 1300 for a recurrent neural network isprovided in FIG. 13. As shown in FIG. 13, the architecture 1300comprises a module 1302 that sequentially performs iterations or cyclesof the machine learning algorithm. During a first iteration or cycle,the module 1302 receives an input x_(i) and generates an output h_(i).The input x_(i) is provided in a time domain, and defines a waveformshape of a portion/segment of the received signal within a contextwindow (e.g., context window 900 of FIG. 9). The output h_(i) comprisesbinary value(s) for symbol(s)/bit(s). During a second iteration orcycle, the module 1302 not only receives as an input the time domaininformation x_(i) but also the previous output h_(i). The module 1202compares a combination of the inputs x_(i), h_(i) to machine learnedmodels to determine the binary value(s) for the desired signal'ssymbol(s)/bit(s), and so on.

Another illustrative architecture for a recurrent neural networkimplementing the present solution is provided in FIG. 14. As shown inFIG. 14, the recurrent neural network 1400 comprises a module 1402 thatsequentially performs iterations or cycles of the machine learningalgorithm. During a first iteration or cycle, the module 1402 receives aplurality of inputs x_(i-d1), x_(i-d2), . . . , x_(i-dV) and generatesan output h_(i). The inputs are provided in different domains. Forexample, input x_(i-d1) is provided in a time domain, and defines awaveform shape of a portion/segment of the received signal within acontext window (e.g., context window 900 of FIG. 9). Input x_(i-d2) isprovided in a phase domain (e.g., defining a change in phase over time),while input x_(i-dV) is provided in an amplitude domain (e.g., definingan average amplitude over time), a frequency domain (e.g., defining achange in frequency over time) or other domain. The inputs x_(i-d2), . .. , x_(i-dV) can be derived using various algorithms that include, butare not limited to, a Fourier transform algorithm, a power spectraldensity algorithm, a wavelet transform algorithm, and/or a spectrogramalgorithm. The module 1402 compares a combination of the inputs tomachine learned models to determine the binary values for the desiredsignal's symbol(s)/bit(s). The inputs x_(i-d1), x_(i-d2), . . . ,x_(i-dV) may be weighted differently by the machine learning algorithmemployed by module 1402. During a second iteration or cycle, the module1402 not only receives inputs x_(i-d1), x_(i-d2), . . . , x_(i-dV) butalso receives the previous output h_(i). The module 1402 compares acombination of the inputs x_(i-d1), x_(i-d2), . . . , x_(i-dV), h_(i) tomachine learned models to determine binary value(s) for the desiredsignal's symbol(s)/bit(s), and so on.

In some scenarios, the recurrent neural network may be implemented by anLSTM algorithm. The LSTM algorithm may be implemented by an LSTM module1502 being applied over and over again for demodulating a group ofsymbols, as shown in FIG. 15. The LSTM module 1502 evolves its weightsover time due to the learning process. During each iteration, the LSTMmodule receives a different portion/segment of a received signal (e.g.,signal 806 of FIGS. 8-10) as an input. For example, during a firstiteration, the LSTM module 1502 is provided portion/segment 1502 of thereceived signal. Portion/segment 1502 is contained in a context window1504 including M symbol periods, where M is an integer (e.g., 10).Portion/segment 1502 is processed by the LSTM module 1502 to recovervalues for the two middle symbols/bits 1506, 1508. During a seconditeration, the LSTM module 1502 is provided portion/segment 1510 of thereceived signal. Portion/segment 1510 is selected by sliding the contextwindow 1504 over by G symbol periods (e.g., 2 symbol periods) as shownin FIG. 15, where G is an integer. Portion/segment 1510 is processed bythe LSTM module 1502 to recover values for the two middle symbols/bits1512, 1514 of the slid context window. Notably, knowledge about thesignal structure learned by the LSTM module 1502 during the firstiteration is used by the LSTM module 1502 during the second iterationfor determining the values for symbols/bits 1512, 1514. This process isrepeated for each next iteration, where the LSTM module uses knowledgeof the symbol/bit values gained by previous iterations. The contextwindow can be slid in two directions — forwards and backwards. In thisway, the LSTM neural network learns the structure over the time of theentire sequence of symbols/bits in multiple directions (i.e., forwardsand backwards). Incorporating LSTM layers in the symboldemodulator/interference canceller 214 can provide robustness channelimperfections/perturbations such as but not limited to Carrier FrequencyOffset (CFO) with an improved Bit Error Rate (BER).

In some scenarios, a plurality of different machine learning algorithmsare employed by the symbol demodulator/interference canceller 214.During each iteration of a symbol/bit decision process, one of themachine learning algorithms may be selected for use in determiningvalues for symbol(s)/bit(s). The machine learning algorithm may beselected based on results of a game theory analysis of the machinelearned models. The following discussion explains an illustrative gametheory analysis.

Typical optimization of a reward matrix in a one-sided, “game againstnature” with a goal of determining the highest minimum gain is performedusing linear programming techniques. In most cases, an optimal result isobtained, but occasionally one or more constraints eliminate possiblefeasible solutions. In this case, a more brute-force subset summingapproach can be used. Subset summing computes the optimal solution bydetermining the highest gain decision after iteratively considering allsubsets of the possible decision alternatives.

A game theory analysis can be understood by considering an exemplarytactical game. Values for the tactical game are presented in thefollowing TABLE 1. The unitless values range from −5 to 5, whichindicate the reward received performing a given action for a particularscenario. The actions for the player correlate in the rows in TABLE 1,while the potential scenarios correlate to the columns in TABLE 1. Forexample, the action of firing a mortar at an enemy truck yields apositive reward of 4, but firing a mortar on a civilian truck yeils anegative reward of −4, i.e., a loss. The solution can be calculated froma linear program, with the results indicating that the best choice forthe play is to advance rather than fire mortar or do nothing. Inexamples with very large reward matrices, the enhancement technique ofsubset summing may also be applied. Since there are four scenarios inthis example (enemy truck, civilian truck, enemy tank, or friendlytank), there are 2⁴=16 subsets of the four scenarios. One of thesesubsets considers none of the decisions, which is impractical. So inpractice, there are always 2^(P)−1 subsets, where P is the number ofcolumns (available scenarios) in a reward matrix. Table 1 is reproducedfrom the following document: Jordan, J. D. (2007). Updating OptimalDecisions Using Game Theory and Exploring Risk Behavior Through ResponseSurface Methodology.

TABLE 1 Enemy Civilian Enemy Friendly Truck Truck Tank Tank Fire Mortar4 −4 5 −5 Advance 1 4 0 4 Do Nothing −1 1 −2 1

The goal of linear programming is to maximize a function over a setconstrained by linear inequalities and the following mathematicalequations (1)-(7).

max z=v+0w ₁+0w ₂+0w ₃   (1)

s.t. v≤4w ₁+1w ₂+−1w ₃   (2)

v≤−4w ₁+4w ₂+1w ₃   (3)

v≤5w ₁+0w ₂+−2w ₃   (4)

v≤−5w ₁+4w ₂+1w ₃   (5)

Σw _(i)=1   (6)

w _(i)≥0∀i   (7)

where z represents the value of the game or the objective function, vrepresents the value of the constraints, w₁ represents the optimalprobability solution for the choice “Fire Mortar”, w₂ represents theoptimal probability solution for the choice “Advance”, w₃ represents theoptimal probability solution for the choice “Do Nothing”, and irepresents the index of decision choice. Using a simplex algorithm tosolve the linear program yields mixed strategy {0.2857, 0.7143, 0}. Tomaximize minimum gain, the player should fire a mortar approximately 29%of the time, advance 71% of the time, and do nothing none of the time.

In scenarios with very large reward matrices, the optional technique ofsubset summing may be applied. The subset summing algorithm reduces aconstrained optimization problem to solving a series of simpler,reduced-dimension constrained optimization problems. Specifically, for areward matrix consisting of P scenarios (columns), a set of 2^(P)−1 newreward matrices are created by incorporating unique subsets of thescenarios. To illustrate the generation of the subsets to be considered,the following mathematical equation (8) shows an example of constraintsfrom the example of TABLE 1 where each row in the equation correspondsto a row in the reward matrix A. Each new reduced reward matrix isformed by multiplying A element-wise by a binary matrix. Each of the2^(P)−1 binary matrices has a unique set of columns which are all-zero.The element-wise multiplication serves to mask out specific scenarios,leaving only specific combinations, or subsets, of the originalscenarios to be considered. This operation increases the run time, butis a necessary trade-off for improved accuracy. This method also ensuresthat the correct answer is found by computing the proper objectivefunction. If, for example, A represents a reward matrix, then thesolution for computing all combinations of rows is:

$\begin{matrix}\begin{matrix}{{A.}*\left\lbrack 1 \right.} & 1 & 0 & 1 \\{\mspace{56mu} 1} & 1 & 0 & 1 \\{\mspace{56mu} 1} & 1 & 0 & \left. 1 \right\rbrack\end{matrix} & (8)\end{matrix}$

One reason for running all combinations of decisions, 2^(P)−1, where Pis the number of columns in a reward matrix, is that one or moreconstraints eliminate(s) possible feasible solutions, as shown in FIG.16 with circles. A feasible region is a graphical solution space for theset of all possible points of an optimization problem that satisfy theproblem's constraints. Information is treated as parameters rather thanconstraints, so that a decision can be made outside of traditionalfeasible regions. This is why the present solution works robustly withcomplex data for general decision-making applications. Note that FIG. 16is a simplified representation that could have as many as P dimensions.

The above TABLE 1 can be modified in accordance with the presentsolution. For example, each row is associated with a respective machinelearned model of a plurality of machine learned models, and each columnis associated with a respective modulation class of a plurality ofmodulation classes. For example, a first machine learned model wasgenerated using a first machine learning algorithm. A second machinelearned model was generated using a second different machine learningalgorithm. A third machine learned model was generated using a thirdmachine learning algorithm that is different from the first and secondmachine learning algorithms. Each cell in the body of the table includesa likelihood score S. The following TABLE 2 illustrates thisconfiguration.

TABLE 2 Modulation Modulation Modulation Modulation Class 1 Class 2Class 3 Class 4 First Machine S₁ S₄ S₇ S₁₀ Learned Model Second MachineS₂ S₅ S₈ S₁₁ Learned Model Third Machine S₃ S₆ S₉ S₁₂ Learned ModelThe likelihood scores can include, but are not limited to,goodness-of-fit-predicted scores calculated based on the number ofsignals and modulation classes. Each goodness-of-fit-predicted scoredescribes how well the machine learned model and modulation class fit aset of observations. A measure of goodness-of-fit summarizes thediscrepancy between observed values and the values expected under themachine learned model in question. The goodness-of-fit-predicted scorecan be determined, for example, using a chi-squared distributionalgorithm and/or a likelihood ratio algorithm. The modulation classescan include, but are not limited to, frequency modulation, amplitudemodulation, phase modulation, angle modulation, and/or line codingmodulation.

The reward matrix illustrated by TABLE 2 can be constructed and solvedusing a linear program. For example, an interior-point algorithm can beemployed. A primal standard form can be used to calculate optimal tasksand characteristics in accordance with the following mathematicalequation (10).

maximize ƒ(x)s.t.   (10)

A(x)≤b

x≥0

Referring now to FIG. 17, there is provided a flow diagram of anillustrative method 1700 for operating a receiver (e.g., receiver 104 ofFIG. 1 and/or 200 of FIG. 2). Method 1700 begins with 1702 and continueswith 1704 where the receiver receives a combined waveform (e.g.,combined waveform 806 of FIG. 8) comprising a combination of a desiredsignal (e.g., desired signal 802 of FIG. 8) and an interference signal(e.g., interference signal 804 of FIG. 8). Next in 1706-1712, thereceiver performs demodulation operations to extract the desired signalfrom the combined waveform. The demodulation operations comprise:obtaining machine learned models for recovering the desired signal fromcombined waveform (e.g., from memory 310 of FIG. 3); optionallyselecting one or more machine learned models based on a given criteria(e.g., based on results of a game theory analysis of the machine learnedmodels); comparing characteristic(s) of the received combined waveformto the machine learned model(s); and determining a value for at leastone symbol of the desired signal based on results of the comparingand/or a previous symbol decision. Subsequently, 1714 is performed wheremethod 1700 ends or other operations are performed (e.g., return to1702).

The machine learned models may be generated using the same machinelearning algorithm or respectively using different machine learningalgorithms. The characteristic(s) of the received combined waveform mayinclude, but is(are) not limited to, a phase characteristic (e.g., aphase change over time), an amplitude characteristic (e.g., an averageamplitude over time), a frequency characteristic (e.g., a change infrequency over time), or a waveform shape.

The results of the comparing may comprise an identification of a machinelearned model that matches the characteristic(s) of the receivedcombined waveform by a given amount (e.g., >50%). The value for thesymbol(s) of the desired signal may be set equal to symbol value(s) thatis(are) associated with the identified machine learned model.

In some scenarios, 1710 comprises comparing characteristic(s) of eachsegment of a plurality of segments of the combined waveform to themachine learned models to identify a machine learned model that matchesthe characteristic(s) of the segment by a given amount. At least twosegments may both comprise information for at least one given symbol ofthe desired signal. Each segment may extend over multiple symbol timeperiods. In 1712, a value for a symbol for the desired signal containedin the segment of the combined waveform may be set equal to a symbolvalue that is associated with the machine learned model that matches thecharacteristic(s) of the segment by the given amount.

The implementing systems of method 1700 may comprise a receiver havingan antenna (e.g., antenna 202 of FIG. 2) and/or a symbol demodulator(e.g., symbol demodulator/interference canceller 214 of FIG. 2). Theantenna is configured to receive the combined waveform comprising acombination of the desired signal and the interference signal. Thesymbol demodulator comprises a processor (e.g., processor 304 of FIG.3), and a non-transitory computer-readable storage medium (e.g., memory310 and/or hardware entities 312 of FIG. 3) comprising programminginstructions (e.g., instructions 320 of FIG. 3) that are configured tocause the processor to implement method 1700.

Although the present solution has been illustrated and described withrespect to one or more implementations, equivalent alterations andmodifications will occur to others skilled in the art upon the readingand understanding of this specification and the annexed drawings. Inaddition, while a particular feature of the present solution may havebeen disclosed with respect to only one of several implementations, suchfeature may be combined with one or more other features of the otherimplementations as may be desired and advantageous for any given orparticular application. Thus, the breadth and scope of the presentsolution should not be limited by any of the above describedembodiments. Rather, the scope of the present solution should be definedin accordance with the following claims and their equivalents.

What is claimed is:
 1. A method for operating a receiver, comprising: receiving, at the receiver, a combined waveform comprising a combination of a desired signal and an interference signal; and performing, by the receiver, demodulation operations to extract the desired signal from the combined waveform, the demodulation operations comprising: obtaining, by the receiver, machine learned models for recovering the desired signal from combined waveforms; comparing, by the receiver, at least one characteristic of the received combined waveform to at least one of the machine learned models; and determining a value for at least one symbol of the desired signal based on results of the comparing.
 2. The method according to claim 1, wherein the machine learned models are generated using a same machine learning algorithm or respectively using different machine learning algorithms.
 3. The method according to claim 1, wherein the at least one characteristic of the received combined waveform comprises a phase characteristic, an amplitude characteristic, a frequency characteristic, or a waveform shape.
 4. The method according to claim 1, wherein the results of the comparing comprise an identification of a machine learned model that matches the at least one characteristic of the received combined waveform by a given amount.
 5. The method according to claim 4, wherein the determining comprises setting the value for the at least one symbol of the desired signal equal to a symbol value that is associated with the identified machine learned model.
 6. The method according to claim 1, wherein the demodulation operations further comprise comparing at least one characteristic of each segment of a plurality of segments of the combined waveform to the machine learned models to identify a machine learned model that matches the at least one characteristic of the segment by a given amount.
 7. The method according to claim 6, wherein at least two segments of the plurality of segments both comprise information for at least one given symbol of the desired signal.
 8. The method according to claim 6, wherein each segment of the plurality of segments extends over multiple symbol time periods.
 9. The method according to claim 6, wherein the demodulation operations further comprise setting a value for at least one symbol for the desired signal contained in the segment of the combined waveform equal to a symbol value that is associated with the machine learned model that matches the at least one characteristic of the segment by the given amount.
 10. The method according to claim 1, wherein the value for the at least one symbol of the desired signal is determined further based on results of a previous symbol decision.
 11. The method according to claim 1, further comprising selecting a machine learned model from the machine learned models for use in said comparing based on results of a game theory analysis of the machine learned models.
 12. A receiver, comprising: a processor; and a non-transitory computer-readable storage medium comprising programming instructions that are configured to cause the processor to implement a method for performing demodulation operations to extract the desired signal from a combined waveform comprising a combination of a desired signal and an interference signal, wherein the programming instructions comprise instructions to: obtain machine learned models for recovering the desired signal from combined waveforms; compare at least one characteristic of the received combined waveform to at least one of the machine learned models; and determine a value for at least one symbol of the desired signal based on results of the comparing.
 13. The receiver according to claim 12, wherein the machine learned models are generated using a same machine learning algorithm or respectively using different machine learning algorithms.
 14. The receiver according to claim 12, wherein the at least one characteristic of the received combined waveform comprises a phase characteristic, an amplitude characteristic, a frequency characteristic, or a waveform shape.
 15. The receiver according to claim 12, wherein the results of the comparing comprise an identification of a machine learned model that matches the at least one characteristic of the received combined waveform by a given amount.
 16. The receiver according to claim 15, wherein the value for the at least one symbol of the desired signal is set equal to a symbol value that is associated with the identified machine learned model.
 17. The receiver according to claim 12, wherein the programming instructions further comprise instructions to compare at least one characteristic of each segment of a plurality of segments of the combined waveform to the machine learned models to identify a machine learned model that matches the at least one characteristic of the segment by a given amount.
 18. The receiver according to claim 17, wherein at least two segments of the plurality of segments both comprise information for at least one given symbol of the desired signal.
 19. The receiver according to claim 17, wherein each segment of the plurality of segments extends over multiple symbol time periods.
 20. The receiver according to claim 17, wherein the programing instructions further comprise instructions to set a value for at least one symbol for the desired signal contained in the segment of the combined waveform equal to a symbol value that is associated with the machine learned model that matches the at least one characteristic of the segment by the given amount.
 21. The receiver according to claim 12, wherein the value for at least one symbol of the desired signal is determined further based on results of a previous symbol decision.
 22. The receiver according to claim 12, wherein the programming instructions further comprise instructions to select a machine learned model from the machine learned models for use in said comparing based on results of a game theory analysis of the machine learned models. 